An alternative methodology for mining seasonal pattern using self-organizing map

Denny, Vincent C.S. Lee

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In retail industry, it is very important to understand seasonal sales pattern, because this knowledge can assist decision makers in managing inventory and formulating marketing strategies. Self-Organizing Map (SOM) is suitable for extracting and illustrating essential structures because SOM has unsupervised learning and topology preserving properties, and prominent visualization techniques. In this experiment, we propose a method for seasonal pattern analysis using Self-Organizing Map. Performance test with real-world data from stationery stores in Indonesia shows that the method is effective for seasonal pattern analysis. The results are used to formulate several marketing and inventory management strategies.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 8th Pacific-Asia Conference, PAKDD 2004, Proceedings
EditorsHonghua Dai, Chengqi Zhang, Ramakrishnan Srikant
PublisherSpringer Verlag
Pages424-430
Number of pages7
ISBN (Print)354022064X, 9783540220640
Publication statusPublished - 1 Jan 2004
Event8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004 - Sydney, Australia
Duration: 26 May 200428 May 2004

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3056
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2004
CountryAustralia
CitySydney
Period26/05/0428/05/04

Keywords

  • Clustering
  • Self-organizing maps
  • Temporal data
  • Visualization

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